The web has become a major tool for communication, services and an outstanding source of knowledge. It has also grown in complexity, and end-users may experience difficulties in reading and acquiring good understanding of some overly complex or poorly designed web pages. This observation is even more valid for people with visual disabilities. In this paper, we focus on people with low or weakening vision, for whom we propose to adapt web pages to their needs, while preserving the spirit of the original design. In this context, obtaining a web page adaptation in a very short time may be a difficult problem, because user and designer needs and preferences may contradict each other, and because there may be a large number of adaptation possibilities. Finding a relevant adaptation in a large search space can hardly be done by an algorithm which computes and assesses all possible solutions, which brings us to consider evolutionary algorithms. A characteristic of our problem is to consider a set of preferences, each being implemented by an evaluation function. This optimization problem can be dealt with multi-objective genetic algorithms, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and its next version (NSGA-III). NSGA-III has been recently introduced to address many-objective optimization problems (having more that four objectives). We compare NSGA-II and NSGA-III performances in the context of adapting web pages in accordance to a set of preferences. The comparison is based on running time, number of generations and quality of computed adaptation (number of satisfied objectives). We also show the importance of several parameters including population size, crossover/mutation probability, and the opportunity to aggregate objective functions. From the obtained results, we conclude that the approach is feasible and effective on realistic web pages, especially with NSGA-III.